Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria

Abstract

PurposeThe 2015 American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification.MethodsWe undertook an iterative process of refining the ACMG-AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions.ResultsOur implementation: (i) separated ambiguous ACMG-AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the "clinical criteria" style of the guidelines with additive, semiquantitative criteria.ConclusionSherloc builds on the strong framework of 33 rules established by the ACMG-AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

Figure 1

Classification scoring thresholds and evidence categories. (a) Point score thresholds for pathogenic (P), likely pathogenic, variant of uncertain significance, likely benign, and benign (B) classifications. Pathogenic and benign evidence is scored separately. Evidence in both directions can suggest a non-Mendelian variant. (b) Five evidence categories in the order in which they are evaluated, and with the point value of select criteria indicated. Clinical criteria include population data and clinical findings. Functional criteria include sequence observations, molecular studies, and indirect and computational data. ExAC, Exome Aggregation Consortium.

Figure 2

Population data: Sherloc criteria and decision tree. (a) A single evidence type criterion from the frequency set of criteria is chosen for each variant. This decision tree guides users to the correct criterion based on the quality and abundance of the Exome Aggregation Consortium (ExAC) data at the locus in question, the mode of inheritance of the gene, and the frequency of the variant in ExAC. Points and directionality (pathogenic versus benign) are indicated in the far right column. (b) Decision tree for using observations of homozygotes in the ExAC database depending on the severity, onset, and penetrance of the biallelic phenotype, and the number of homozygotes present. Loci flagged with data quality issues are excluded. Solid orange color corresponds to pathogenic evidence, solid green corresponds to benign evidence, and solid grey corresponds to neutrally weighted evidence. AD, autosomal dominant; AR, autosomal recessive.

Figure 3

Root decision tree for clinical case report criteria. Case reports are divided into one of three types based on the affected status of the proband, the relevance of the phenotype to the gene in question, and the presence of a known disease etiology. This root decision tree guides the user to the correct detailed decision tree (Supplementary Figures S2–S4) based on these considerations. The variant frequency is an essential lens through which to understand the relevance of case reports. The more frequent a variant is, the more likely it becomes that case reports are simply coincidental.

Figure 4

Functional data: Sherloc criteria and decision tree. Functional evidence is evaluated based on the type of experiment performed and the relevance and validity of the assay. Clinical Laboratory Improvement Amendments–generated biochemical data from affected individuals are also considered a type of in vivo functional experiment, although this evidence type is usually used to augment the value of a case report.

Figure 5

Hierarchical approach to efficient variant research. Because a hierarchical relationship exists between evidence types, an ordered approach to the evaluation of evidence can be very efficient. Evidence is evaluated starting with the simplest and potentially most powerful types and working toward the most complicated and subtle types (i.e., from population data and variant type toward clinical data and functional/prediction data). When sufficient evidence for a confident classification is identified, the remaining research can be focused on looking for contradictory evidence.